An integrated microfluidic, metal oxide semiconductor gas sensor combined with machine learning optimization for multiplexed greenhouse gas detection

重要意义：地球大气中的人为温室气体(GHG)排放导致大气逐渐升温，对人类健康造成严重损害，并对经济产生负面影响。因此，为了防止环境恶化，有必要对二氧化碳(CO2)和甲烷(CH4)这两种最丰富的温室气体等温室气体进行持续的大气监测。气相色谱和质谱仪是分析温室气体最常用的技术。然而，这些方法昂贵、耗时，并且需要笨重的设备和高技能的人员。最近的研究和工业努力集中在用基于芯片实验室(LOC)的传感器来取代传统的分析技术来检测温室气体。基于LOC的传感器提供了替代的小型化分析工具，具有提高准确性、减少处理时间和成本等优点。此外，可以在单个系统上进行完整的分析，增强了便携性。创新与目标：我们的长期目标是开发一种“智能气体传感”技术，使之能够便携、低成本、高选择性和高效地检测温室气体。与行业合作伙伴Pro-Flange合作，我们将开发一种超灵敏的微流控金属氧化物半导体传感器，可以检测二氧化碳和甲烷。提出的传感器将在微通道几何形状、传感器恢复时间以及用于CH4和CO2的选择性涂层材料方面进行表征，以实时检测微小浓度(ppm水平)。通过在微通道表面实施新型涂层和优化微流控通道的几何形状，将实现更高的选择性。通过在MOS上电沉积纳米结构来增加比表面积，可以提高MOS的灵敏度。此外，我们将通过从文献和我们的传感器收集气体数据来开发采用模式识别方法的机器学习(ML)算法，以优化传感器设计。

Significance: Anthropogenic greenhouse gas (GHG) emissions in the earth's atmosphere have resulted in the gradual heating of the atmosphere, causing severe damage to human health, and negatively impacting the economy. The continuous atmospheric monitoring of the GHGs, such as carbon dioxide (CO2) and methane (CH4), the two most abundant GHGs, is thus necessary to prevent environmental deterioration. Gas chromatography and mass spectrometry are the most commonly used techniques for analyzing GHGs. However, these methods are expensive, time-consuming, and require bulky devices and highly skilled personnel. Recent research and industrial efforts have focused on replacing the conventional analysis techniques with the lab-on-chip (LOC)-based sensors for detecting GHGs. LOC-based sensors offer alternative miniaturized analysis tools, with advantages, such as enhancing accuracy and reducing processing time and cost. In addition, a complete analysis can be performed on a single system, enhancing portability.Innovation & Objectives: Our long-term goal is to develop a "smart gas sensing" technique that enables portable, low-cost, highly selective, and efficient detection of GHGs. In collaboration with the industry partner, Pro-Flange, we will develop an ultrasensitive microfluidic, metal oxide semiconductor sensor that can detect CO2 and CH4. The proposed sensor will be characterized in terms of microchannel geometry, sensor recovery time, and selective coating materials for CH4 and CO2 to detect small concentrations (ppm level) in real-time. Enhanced selectivity will be achieved by implementing novel coatings on the microchannel surface and optimizing the microfluidic channel geometry. The sensitivity will be improved by electrodepositing nanostructures onto the MOS to increase the surface area. In addition, we will develop a machine learning (ML) algorithm employing pattern recognition methods by collecting gas data from the literature and our sensor to optimize the sensor design.